Worms (Caenorhabditis elegans) and zebrafish (Danio rerio) are premier animal models that have provided profound insight into metabolic processes associated with disease. As such, a major fraction of worm and zebrafish research focuses on metabolism. Since 2010, ~70% of worm and zebrafish publications have been related to metabolism. Strikingly, however, <1% of these publications have utilized metabolomics. Metabolomics is a relatively new technology that offers significant benefits for studying metabolism relative to any other approach. Advantages include unprecedented sensitivity, superior quantitative accuracy, and dramatically improved compound coverage in a high-throughout fashion. The question then arises why metabolomic technologies have been so vastly underutilized in worm and zebrafish research. With the increasing number of metabolomic facilities that have been established over the last decade, the barrier is not related to availability. Rather, the field has been inhibited by the challenges of data interpretation. When an untargeted metabolomic experiment is performed with liquid chromatography/mass spectrometry (LC/MS), ~40k signals are detected from animal samples. Yet, even with the most advanced bioinformatic tools, the majority of signals remain unidentified. This severely limits biological interpretation of the data. We have developed a suite of innovative approaches to annotate each signal in LC/MS untargeted metabolomic data sets. Our work has revealed that the ~40k signals detected by LC/MS only correspond to a few thousand non-redundant, unique metabolites. Here we propose to identify all unique metabolites that can be detected by LC/MS to generate comprehensive C. elegans and zebrafish reference metabolomes. We will then configure LC/MS parameters that enable each detectable metabolite in the worm and zebrafish reference metabolome to be analyzed with a targeted experiment. Unlike untargeted experiments, targeted experiments have the major advantage of providing automated identification and quantitation. Thus, worm and zebrafish workers will be able to use our resource to quantify thousands of biochemically named metabolites without the barrier of complex data analysis that has hindered the field. This will significantly extend the accessibility of metabolomics to worms, fish, and other model animal researchers. To increase the comprehensive coverage of our resource, we will integrate untargeted metabolomic data annotations from worms and zebrafish under various conditions into each reference metabolome. These experiments will provide an opportunity to answer some interesting questions: How do metabolites change during development? How do metabolites change between cell types within an animal? How do metabolites change with stress? What fraction of the reference metabolomes is shared between animals? We expect that the shared metabolome will be substantial and that the methods we develop here to automate analysis of the worm and zebrafish reference metabolome will therefore be broadly applicable to all animal model research.